{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas 进阶修炼 ｜早起Python\n",
    "<br>\n",
    "\n",
    "**本习题由公众号【早起Python & 可视化图鉴】 原创，转载及其他形式合作请与我们联系（微信号`sshs321`)，未经授权严禁搬运及二次创作，侵权必究！**\n",
    "\n",
    "\n",
    "\n",
    "本习题基于 `pandas` 版本 `1.1.3`，所有内容应当在 `Jupyter Notebook` 中执行以获得最佳效果。\n",
    "\n",
    "不同版本之间写法可能会有少许不同，如若碰到此情况，你应该学会如何自行检索解决。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 - pandas 个性化显示设置\n",
    "<br>\n",
    "\n",
    "在使用 `pandas` 时，有时默认的配置方案并不能让我们舒服的进行数据分析。\n",
    "\n",
    "幸运的是，`pandas` 也支持我们 <font color=#E36C07>**自定义显示、样式等个性化操作**</font>。\n",
    "\n",
    "本节将部分常用的设置整理为习题形式，<font color=#E36C07>  **所有操作答案拿走即用。既可以刷一遍来了解有这样那样的设置，也可以保存用于速查手册** </font>  \n",
    "\n",
    "注意：本习题中未提及的配置可以点击查阅 `pandas` 👉 [**官方文档对应文章**](https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html)\n",
    "\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 初始化\n",
    "\n",
    "<br>\n",
    "\n",
    "该 `Notebook` 版本为**纯习题版**\n",
    "\n",
    "如果需要答案或者提示，可以微信搜索公众号「早起Python」获取！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入 `pandas` 并读取当前目录下 `csv` 数据(`data.csv`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-1 基于 option 修改显示设置\n",
    "<br>\n",
    "\n",
    "在 `pandas` 中有一个 `option` 系统，可以通过 `set_option`方法进行进阶显示选项设置。\n",
    "\n",
    "本小节主要整理了一些基于 `option` 修改数据显示的设置。\n",
    "\n",
    "注意【**基于 option 修改显示设置**】并未修改数据，仅是在原有数据基础上优化显示状态，随时可以通过重置选项重置全部设置，恢复数据默认显示状态。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 显示全部列\n",
    "\n",
    "<br>\n",
    "如下图所示👇，直接查看 `data` 会发现，由于数据维度较大，部分行列会被折叠，显示为`...`，现在需要显示全部的列方便预览。\n",
    "\n",
    "![](http://liuzaoqi.oss-cn-beijing.aliyuncs.com/2021/08/19/16293397012946.jpg?域名/sample.jpg?x-oss-process=style/stylename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在下面的 cell 中输入你的解决方案，并在最后执行 `data.head()`以检查你的答案是否正确解决问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   positionId positionName  companyId companySize industryField financeStage  \\\n",
      "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
      "1     5204912         数据建模      50735    150-500人            电商           B轮   \n",
      "2     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
      "\n",
      "                   companyLabelList  firstType secondType thirdType  \\\n",
      "0  ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析   \n",
      "1  ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模   \n",
      "2  ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析   \n",
      "\n",
      "                    skillLables                            positionLables  \\\n",
      "0  ['SQL', '数据库', '数据运营', 'BI']  ['电商', '社交', 'SQL', '数据库', '数据运营', 'BI']   \n",
      "1                ['算法', '数据架构']                            ['算法', '数据架构']   \n",
      "2        ['数据库', '数据分析', 'SQL']                            ['数据库', 'SQL']   \n",
      "\n",
      "                             industryLables       createTime formatCreateTime  \\\n",
      "0  ['电商', '社交', 'SQL', '数据库', '数据运营', 'BI']  2020/3/16 11:00          11:00发布   \n",
      "1                                        []  2020/3/16 11:08          11:08发布   \n",
      "2                                        []  2020/3/16 10:33          10:33发布   \n",
      "\n",
      "  district    businessZones  salary workYear jobNature education  \\\n",
      "0      余杭区           ['仓前']   37500     1-3年        全职        本科   \n",
      "1      滨江区     ['西兴', '长河']   15000     3-5年        全职        本科   \n",
      "2      江干区  ['四季青', '钱江新城']    3500     1-3年        全职        本科   \n",
      "\n",
      "     positionAdvantage   imState        lastLogin  publisherId  approve  \\\n",
      "0  五险一金、弹性工作、带薪年假、年度体检     today  2020/3/16 11:00     12022406        1   \n",
      "1       六险一金,定期体检,丰厚年终  disabled  2020/3/16 11:08      5491688        1   \n",
      "2   五险一金 周末双休 不加班 节日福利     today  2020/3/16 10:33      5322583        1   \n",
      "\n",
      "  subwayline stationname                linestaion   latitude   longitude  \\\n",
      "0        NaN         NaN                       NaN  30.278421  120.005922   \n",
      "1        NaN         NaN                       NaN  30.188041  120.201179   \n",
      "2        4号线         江锦路  4号线_城星路;4号线_市民中心;4号线_江锦路  30.241521  120.212539   \n",
      "\n",
      "  hitags  resumeProcessRate  resumeProcessDay  score  newScore  matchScore  \\\n",
      "0    NaN                 50                 1    233         0   15.101875   \n",
      "1    NaN                 23                 1    176         0   32.559414   \n",
      "2    NaN                 11                 4     80         0   14.972357   \n",
      "\n",
      "   matchScoreExplain  query  explain  isSchoolJob  adWord  plus  pcShow  \\\n",
      "0                NaN    NaN      NaN            0       0   NaN       0   \n",
      "1                NaN    NaN      NaN            0       0   NaN       0   \n",
      "2                NaN    NaN      NaN            0       0   NaN       0   \n",
      "\n",
      "   appShow  deliver  gradeDescription  promotionScoreExplain  isHotHire  \\\n",
      "0        0        0               NaN                    NaN          0   \n",
      "1        0        0               NaN                    NaN          0   \n",
      "2        0        0               NaN                    NaN          0   \n",
      "\n",
      "   count aggregatePositionIds  famousCompany  \n",
      "0      0                   []          False  \n",
      "1      0                   []          False  \n",
      "2      0                   []          False  \n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_columns',None)\n",
    "print(data.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 显示指定行/列\n",
    "\n",
    "<br>\n",
    "\n",
    "指定让 `data` 在预览时显示10列，7行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    positionId positionName  companyId companySize industryField  ...  \\\n",
      "0      6802721         数据分析     475770     50-150人      移动互联网,电商  ...   \n",
      "1      5204912         数据建模      50735    150-500人            电商  ...   \n",
      "2      6877668         数据分析     100125     2000人以上    移动互联网,企业服务  ...   \n",
      "..         ...          ...        ...         ...           ...  ...   \n",
      "7      6764018      数据建模工程师      13163   500-2000人         移动互联网  ...   \n",
      "8      6458372       数据分析专家      34132    150-500人     数据服务,广告营销  ...   \n",
      "9      6786904        数据分析师      13163   500-2000人         移动互联网  ...   \n",
      "\n",
      "   promotionScoreExplain isHotHire count aggregatePositionIds famousCompany  \n",
      "0                    NaN         0     0                   []         False  \n",
      "1                    NaN         0     0                   []         False  \n",
      "2                    NaN         0     0                   []         False  \n",
      "..                   ...       ...   ...                  ...           ...  \n",
      "7                    NaN         0     0                   []          True  \n",
      "8                    NaN         0     0                   []         False  \n",
      "9                    NaN         0     0                   []          True  \n",
      "\n",
      "[10 rows x 52 columns]\n"
     ]
    }
   ],
   "source": [
    "# 设置最大显示列数为10\n",
    "pd.set_option('display.max_columns', 10)\n",
    "\n",
    "# 设置最大显示行数为7\n",
    "pd.set_option('display.max_rows', 7)\n",
    "\n",
    "# （可选）禁止在宽度不足时折叠显示，确保所有列都显示出来\n",
    "# pd.set_option('display.expand_frame_repr', False)\n",
    "\n",
    "# （可选）设置一个足够大的显示宽度，减少自动折行的可能\n",
    "# pd.set_option('display.width', 200)\n",
    "\n",
    "print(data.head(10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 还原行/列显示数\n",
    "\n",
    "<br>\n",
    "还原上面的显示设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   positionId positionName  companyId companySize industryField financeStage  \\\n",
      "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
      "1     5204912         数据建模      50735    150-500人            电商           B轮   \n",
      "2     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
      "3     6496141         数据分析      26564   500-2000人            电商        D轮及以上   \n",
      "4     6467417         数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
      "\n",
      "                    companyLabelList  firstType secondType thirdType  ...  \\\n",
      "0   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析  ...   \n",
      "1   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模  ...   \n",
      "2   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析  ...   \n",
      "3  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发      数据分析  ...   \n",
      "4   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析  ...   \n",
      "\n",
      "  plus pcShow appShow deliver gradeDescription promotionScoreExplain  \\\n",
      "0  NaN      0       0       0              NaN                   NaN   \n",
      "1  NaN      0       0       0              NaN                   NaN   \n",
      "2  NaN      0       0       0              NaN                   NaN   \n",
      "3  NaN      0       0       0              NaN                   NaN   \n",
      "4  NaN      0       0       0              NaN                   NaN   \n",
      "\n",
      "  isHotHire  count aggregatePositionIds famousCompany  \n",
      "0         0      0                   []         False  \n",
      "1         0      0                   []         False  \n",
      "2         0      0                   []         False  \n",
      "3         0      0                   []          True  \n",
      "4         0      0                   []          True  \n",
      "\n",
      "[5 rows x 52 columns]\n"
     ]
    }
   ],
   "source": [
    "pd.reset_option('^display')\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面这个表格汇总了主要的还原方法及其适用场景：\n",
    "\n",
    "| 还原方法 | 功能描述 | 适用场景 |\n",
    "| :--- | :--- | :--- |\n",
    "| `pd.reset_option(\"all\")` | 重置**所有**Pandas选项为默认值 | 需要全面恢复默认设置时 |\n",
    "| `pd.reset_option(\"^display\")` | 重置所有以`display`开头的选项 | 只想重置显示相关的设置时 |\n",
    "| `pd.reset_option(\"max_rows\")` / `pd.reset_option(\"max_columns\")` | 重置特定的单个选项 | 只需还原某个特定设置时 |\n",
    "| 使用`with pd.option_context(...):` | 临时修改设置，退出代码块后**自动还原** | 仅在代码块内需要特殊显示格式时 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 修改每列最大字符宽度\n",
    "\n",
    "<br>\n",
    "\n",
    "即每列最多显示的字符长度，例如【每列最多显示10个字符，多余的会变成`...`】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   positionId positionName  companyId companySize industryField financeStage  \\\n",
      "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
      "1     5204912         数据建模      50735    150-500人            电商           B轮   \n",
      "2     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
      "3     6496141         数据分析      26564   500-2000人            电商        D轮及以上   \n",
      "4     6467417         数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
      "\n",
      "      companyLabelList  firstType secondType thirdType  ... plus pcShow  \\\n",
      "0  ['绩效奖金', '带薪年假',...  产品|需求|项目类       数据分析      数据分析  ...  NaN      0   \n",
      "1  ['年终奖金', '做五休二',...  开发|测试|运维类       数据开发        建模  ...  NaN      0   \n",
      "2  ['节日礼物', '年底双薪',...  产品|需求|项目类       数据分析      数据分析  ...  NaN      0   \n",
      "3  ['生日趴', '每月腐败基金'...  开发|测试|运维类       数据开发      数据分析  ...  NaN      0   \n",
      "4  ['技能培训', '免费班车',...  产品|需求|项目类       数据分析      数据分析  ...  NaN      0   \n",
      "\n",
      "  appShow deliver gradeDescription promotionScoreExplain isHotHire  count  \\\n",
      "0       0       0              NaN                  NaN          0      0   \n",
      "1       0       0              NaN                  NaN          0      0   \n",
      "2       0       0              NaN                  NaN          0      0   \n",
      "3       0       0              NaN                  NaN          0      0   \n",
      "4       0       0              NaN                  NaN          0      0   \n",
      "\n",
      "  aggregatePositionIds famousCompany  \n",
      "0                   []         False  \n",
      "1                   []         False  \n",
      "2                   []         False  \n",
      "3                   []          True  \n",
      "4                   []          True  \n",
      "\n",
      "[5 rows x 52 columns]\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth',20)\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面这个表格汇总了常用的核心配置选项及其作用，方便您快速查阅。\n",
    "\n",
    "| 配置类别 | 选项参数 | 功能描述 | 常用设置值示例 |\n",
    "| :--- | :--- | :--- | :--- |\n",
    "| **字符串显示** | `display.max_colwidth` | 设置单列字符串的最大显示宽度（字符数），超出部分用省略号表示。 | `100` 或 `None` (不截断)  |\n",
    "| **小数精度** | `display.precision` | 统一设置浮点数的小数位显示精度。 | `2` (保留两位小数)  |\n",
    "| | `display.float_format` | 通过格式字符串全局自定义所有浮点数列的显示格式。 | `'{:.2f}'.format`  |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 修改小数点精度\n",
    "\n",
    "<br>\n",
    "\n",
    "修改默认显示精度为小数点后5位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   latitude\n",
      "0  30.27842\n",
      "1  30.18804\n",
      "2  30.24152\n",
      "3  30.29940\n",
      "4  30.28295\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.precision',5)\n",
    "print(data.head()[['latitude']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6 还原所有显示设置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "还原上面的全部显示设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Windows\\Temp\\ipykernel_20820\\1828240519.py:1: FutureWarning: data_manager option is deprecated and will be removed in a future version. Only the BlockManager will be available.\n",
      "  pd.reset_option('all')\n",
      "C:\\Windows\\Temp\\ipykernel_20820\\1828240519.py:1: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  pd.reset_option('all')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   positionId positionName  companyId companySize industryField financeStage  \\\n",
      "0     6802721         数据分析     475770     50-150人      移动互联网,电商           A轮   \n",
      "1     5204912         数据建模      50735    150-500人            电商           B轮   \n",
      "2     6877668         数据分析     100125     2000人以上    移动互联网,企业服务         上市公司   \n",
      "3     6496141         数据分析      26564   500-2000人            电商        D轮及以上   \n",
      "4     6467417         数据分析      29211     2000人以上         物流丨运输         上市公司   \n",
      "\n",
      "                    companyLabelList  firstType secondType thirdType  ...  \\\n",
      "0   ['绩效奖金', '带薪年假', '定期体检', '弹性工作']  产品|需求|项目类       数据分析      数据分析  ...   \n",
      "1   ['年终奖金', '做五休二', '六险一金', '子女福利']  开发|测试|运维类       数据开发        建模  ...   \n",
      "2   ['节日礼物', '年底双薪', '股票期权', '带薪年假']  产品|需求|项目类       数据分析      数据分析  ...   \n",
      "3  ['生日趴', '每月腐败基金', '每月补贴', '年度旅游']  开发|测试|运维类       数据开发      数据分析  ...   \n",
      "4   ['技能培训', '免费班车', '专项奖金', '岗位晋升']  产品|需求|项目类       数据分析      数据分析  ...   \n",
      "\n",
      "  plus pcShow appShow deliver gradeDescription promotionScoreExplain  \\\n",
      "0  NaN      0       0       0              NaN                   NaN   \n",
      "1  NaN      0       0       0              NaN                   NaN   \n",
      "2  NaN      0       0       0              NaN                   NaN   \n",
      "3  NaN      0       0       0              NaN                   NaN   \n",
      "4  NaN      0       0       0              NaN                   NaN   \n",
      "\n",
      "  isHotHire  count aggregatePositionIds famousCompany  \n",
      "0         0      0                   []         False  \n",
      "1         0      0                   []         False  \n",
      "2         0      0                   []         False  \n",
      "3         0      0                   []          True  \n",
      "4         0      0                   []          True  \n",
      "\n",
      "[5 rows x 52 columns]\n"
     ]
    }
   ],
   "source": [
    "pd.reset_option('all')\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-2 更多 option 相关设置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 忽略警告\n",
    "<br>\n",
    "\n",
    "取消`pandas`相关`warning`提示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Windows\\Temp\\ipykernel_20820\\3540492187.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_products['price'] = filtered_products['price'] * 1.1  # 涨价10%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 创建示例数据\n",
    "demo_data = {\n",
    "    'product': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "    'category': ['electronics', 'clothing', 'electronics', 'clothing', 'electronics', 'clothing'],\n",
    "    'price': [100, 50, 200, 80, 150, 60]\n",
    "}\n",
    "df = pd.DataFrame(demo_data)\n",
    "\n",
    "# ❌ 错误做法：链式索引赋值\n",
    "# pd.set_option('mode.chained_assignment', 'warn')  # 启用警告\n",
    "pd.set_option('mode.chained_assignment', None)  # 关闭警告\n",
    "filtered_products = df[df['category'] == 'electronics']\n",
    "filtered_products['price'] = filtered_products['price'] * 1.1  # 涨价10%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8 设置数值显示条件"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果数值小于 20 则显示为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A      B\n",
      "0  15    0.0\n",
      "1  25   22.1\n",
      "2   5    0.0\n",
      "3  35  100.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 设置小于20的数值显示为0（仅对浮点数生效）\n",
    "pd.set_option('display.chop_threshold', 20)\n",
    "\n",
    "# 示例：创建一个包含不同数值的DataFrame进行测试\n",
    "df = pd.DataFrame({'A': [15, 25, 5, 35], 'B': [18.5, 22.1, 19.9, 100]})\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9 让 pandas 支持 LaTex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让`dataframe`中内容支持 `Latex` 显示（需要使用`$$`包住）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始DataFrame:\n",
      "         Expression  Value\n",
      "0   a^2 + b^2 = c^2    0.0\n",
      "1  e^{i\\pi} + 1 = 0    0.0\n",
      "2          \\sqrt{2}    0.0\n",
      "3     x_{1} + y_{1}    0.0\n",
      "\n",
      "生成的LaTeX代码:\n",
      "\\begin{table}\n",
      "\\caption{示例表格：包含LaTeX数学公式}\n",
      "\\label{tab:math_example}\n",
      "\\begin{tabular}{l|r}\n",
      "\\toprule\n",
      " & Expression & Value \\\\\n",
      "\\midrule\n",
      "0 & $a^2 + b^2 = c^2$ & $5.12$ \\\\\n",
      "1 & $e^{i\\pi} + 1 = 0$ & $3.14$ \\\\\n",
      "2 & $\\sqrt{2}$ & $1.41$ \\\\\n",
      "3 & $x_{1} + y_{1}$ & $10.00$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 实际无效，以后再研究\n",
    "import pandas as pd\n",
    "\n",
    "# 1. 创建一个示例DataFrame\n",
    "# 假设'Expression'列包含LaTeX数学表达式字符串，'Value'列包含数值\n",
    "df = pd.DataFrame({\n",
    "    'Expression': ['a^2 + b^2 = c^2', 'e^{i\\\\pi} + 1 = 0', '\\\\sqrt{2}', 'x_{1} + y_{1}'],\n",
    "    'Value': [5.12345, 3.14159, 1.41421, 10]\n",
    "})\n",
    "print(\"原始DataFrame:\")\n",
    "print(df)\n",
    "\n",
    "# 2. 定义自定义格式化函数\n",
    "def to_math_mode(x):\n",
    "    \"\"\"\n",
    "    将输入值x用LaTeX数学环境$$包裹。\n",
    "    如果x是字符串，直接包裹；如果是数字，先转换为字符串。\n",
    "    \"\"\"\n",
    "    # 根据你的需求，可以在这里添加更复杂的逻辑来判断是否需要包裹\n",
    "    # 例如，如果字符串已经包含$，或者不包含任何数学符号，可以选择不包裹\n",
    "    # 这是一个基础示例，为所有应用此函数的单元格值添加$$\n",
    "    if pd.notna(x):  # 处理非空值\n",
    "        return f\"${str(x)}$\"  # 注意：这里使用一对$，在LaTeX中表示行内数学模式\n",
    "    else:\n",
    "        return x  # 如果是空值，原样返回\n",
    "\n",
    "# 3. 使用Styler应用格式化并转换为LaTeX\n",
    "# 创建一个Styler对象，并为'Expression'列应用自定义格式化函数\n",
    "styled_df = df.style.format({\n",
    "    'Expression': to_math_mode,  # 将函数应用到'Expression'列\n",
    "    # 你也可以为其他列应用不同的格式化，例如将Value列格式化为保留两位小数\n",
    "    'Value': lambda x: f\"${x:.2f}$\" if pd.notna(x) else x  # 同时将数值也放入$中并格式化\n",
    "})\n",
    "\n",
    "# 4. 生成LaTeX代码\n",
    "latex_code = styled_df.to_latex(\n",
    "    hrules=True,  # 添加水平线，使表格更美观\n",
    "    column_format='l|r',  # 设置列格式，第一列左对齐，第二列右对齐，中间用竖线分隔\n",
    "    caption=\"示例表格：包含LaTeX数学公式\",\n",
    "    label=\"tab:math_example\"\n",
    ")\n",
    "\n",
    "print(\"\\n生成的LaTeX代码:\")\n",
    "print(latex_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 修改默认绘图引擎\n",
    "\n",
    "修改`pandas`默认绘图引擎为`plotly`（需要提前安装好`plotly`）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[32], line 22\u001b[0m\n\u001b[0;32m     15\u001b[0m fig\u001b[38;5;241m.\u001b[39mupdate_layout(\n\u001b[0;32m     16\u001b[0m     title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m我的第一个Pandas+Plotly图表\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     17\u001b[0m     xaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mX轴\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     18\u001b[0m     yaxis_title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mY轴\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m     19\u001b[0m )\n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# 显示图表\u001b[39;00m\n\u001b[1;32m---> 22\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Soft\\Python\\Python310\\lib\\site-packages\\plotly\\basedatatypes.py:3420\u001b[0m, in \u001b[0;36mBaseFigure.show\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3387\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3388\u001b[0m \u001b[38;5;124;03mShow a figure using either the default renderer(s) or the renderer(s)\u001b[39;00m\n\u001b[0;32m   3389\u001b[0m \u001b[38;5;124;03mspecified by the renderer argument\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3416\u001b[0m \u001b[38;5;124;03mNone\u001b[39;00m\n\u001b[0;32m   3417\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   3418\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpio\u001b[39;00m\n\u001b[1;32m-> 3420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pio\u001b[38;5;241m.\u001b[39mshow(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32md:\\Soft\\Python\\Python310\\lib\\site-packages\\plotly\\io\\_renderers.py:415\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[0;32m    410\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    411\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    412\u001b[0m     )\n\u001b[0;32m    414\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat\u001b[38;5;241m.\u001b[39m__version__) \u001b[38;5;241m<\u001b[39m Version(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m4.2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 415\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    416\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    417\u001b[0m     )\n\u001b[0;32m    419\u001b[0m display_jupyter_version_warnings()\n\u001b[0;32m    421\u001b[0m ipython_display\u001b[38;5;241m.\u001b[39mdisplay(bundle, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[1;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    }
   ],
   "source": [
    "# 仍不生效，以后再说\n",
    "import pandas as pd\n",
    "import plotly.express as px  # 虽然不是必须，但显式导入plotly是良好实践\n",
    "\n",
    "# 设置全局后端\n",
    "pd.options.plotting.backend = 'plotly'\n",
    "\n",
    "# 创建示例数据\n",
    "df = pd.DataFrame({'A': [1, 3, 2], 'B': [3, 2, 1]})\n",
    "\n",
    "# 绘制图表：明确指定图表类型为线图\n",
    "fig = df.plot.line()  # 使用 .line() 而非默认的 .plot()\n",
    "\n",
    "# 自定义布局（可选，让图表更美观）\n",
    "fig.update_layout(\n",
    "    title='我的第一个Pandas+Plotly图表',\n",
    "    xaxis_title='X轴',\n",
    "    yaxis_title='Y轴'\n",
    ")\n",
    "\n",
    "# 显示图表\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11 还原所有 option 设置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "还原上面全部 option 设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Windows\\Temp\\ipykernel_20820\\2786130087.py:1: FutureWarning:\n",
      "\n",
      "data_manager option is deprecated and will be removed in a future version. Only the BlockManager will be available.\n",
      "\n",
      "C:\\Windows\\Temp\\ipykernel_20820\\2786130087.py:1: FutureWarning:\n",
      "\n",
      "use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.reset_option('all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 彩蛋"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如何设置在预览数据时，不换行显示每列内容？\n",
    "\n",
    "![](http://liuzaoqi.oss-cn-beijing.aliyuncs.com/2021/08/19/16293394983242.jpg?域名/sample.jpg?x-oss-process=style/stylename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-3 基于 style  个性化设置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面基于 `option` 的 `pandas` 相关设置是<font color=#E36C07>全局配置</font>，一次设置会在关闭notebook前一直有效\n",
    "\n",
    "但相关常用的设置并不多，不能满足更多的个性化需求。\n",
    "\n",
    "幸运的是在 `pandas` 中提供 `Styler` 对象让我们进一步个性化展示数据。\n",
    "\n",
    "本节我就将一些常用的基于 `style` 个性化设置整理为习题模式方便大家学习、巩固。\n",
    "\n",
    "注意：基于 `style` 个性化设置<font color=#E36C07>**同样不会修改数据**</font>，所有 `data.style.xxxx` 输出的数据均是<font color=#E36C07>一次性的（可以复用、导出）</font>，因此你应该在合适的时间选择使用该方法。\n",
    "\n",
    "下面仅列举常用的方法，若想了解更多可以查阅[**pandas官方文档对应文章👉**](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  重新加载数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了方便理解，重新读取`data.csv`**前20行指定列**\n",
    "- `'positionName'`\n",
    "- `'createTime'`（设置为时间格式）\n",
    "- `'salary'`\n",
    "- `'subwayline'`\n",
    "- `'matchScore'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data.csv\", \n",
    "                   usecols=['positionName', 'createTime', 'salary', 'subwayline', 'matchScore'], \n",
    "                   nrows=20, \n",
    "                   parse_dates=['createTime'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12 隐藏索引\n",
    "\n",
    "<br>\n",
    "\n",
    "隐藏索引列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_f969d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_f969d_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_f969d_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_f969d_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_f969d_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_f969d_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_f969d_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_f969d_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_f969d_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_f969d_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_f969d_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_f969d_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_f969d_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_f969d_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_f969d_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_f969d_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_f969d_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_f969d_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_f969d_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_f969d_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_f969d_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_f969d_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_f969d_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_f969d_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_f969d_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_f969d_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_f969d_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_f969d_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_f969d_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_f969d_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_f969d_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_f969d_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_f969d_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_f969d_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_f969d_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_f969d_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_f969d_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_f969d_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_f969d_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_f969d_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_f969d_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_f969d_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_f969d_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_f969d_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_f969d_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_f969d_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_f969d_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_f969d_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_f969d_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_f969d_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_f969d_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_f969d_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_f969d_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_f969d_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_f969d_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_f969d_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_f969d_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_f969d_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_f969d_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_f969d_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_f969d_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_f969d_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_f969d_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_f969d_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_f969d_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_f969d_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_f969d_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_f969d_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_f969d_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_f969d_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_f969d_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_f969d_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_f969d_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_f969d_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_f969d_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b59dbaf0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 注意版本\n",
    "data.style.hide()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13 调整精度\n",
    "\n",
    "<br>\n",
    "\n",
    "将带有小数点的列精度调整为小数点后2位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_15353\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_15353_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_15353_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_15353_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_15353_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_15353_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_15353_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_15353_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_15353_row0_col2\" class=\"data row0 col2\" >37500.00</td>\n",
       "      <td id=\"T_15353_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row0_col4\" class=\"data row0 col4\" >15.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_15353_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_15353_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_15353_row1_col2\" class=\"data row1 col2\" >15000.00</td>\n",
       "      <td id=\"T_15353_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row1_col4\" class=\"data row1 col4\" >32.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_15353_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_15353_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_15353_row2_col2\" class=\"data row2 col2\" >3500.00</td>\n",
       "      <td id=\"T_15353_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_15353_row2_col4\" class=\"data row2 col4\" >14.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_15353_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_15353_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_15353_row3_col2\" class=\"data row3 col2\" >45000.00</td>\n",
       "      <td id=\"T_15353_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_15353_row3_col4\" class=\"data row3 col4\" >12.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_15353_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_15353_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_15353_row4_col2\" class=\"data row4 col2\" >30000.00</td>\n",
       "      <td id=\"T_15353_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row4_col4\" class=\"data row4 col4\" >12.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_15353_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_15353_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_15353_row5_col2\" class=\"data row5 col2\" >50000.00</td>\n",
       "      <td id=\"T_15353_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row5_col4\" class=\"data row5 col4\" >12.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_15353_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_15353_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_15353_row6_col2\" class=\"data row6 col2\" >30000.00</td>\n",
       "      <td id=\"T_15353_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row6_col4\" class=\"data row6 col4\" >12.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_15353_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_15353_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_15353_row7_col2\" class=\"data row7 col2\" >35000.00</td>\n",
       "      <td id=\"T_15353_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_15353_row7_col4\" class=\"data row7 col4\" >3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_15353_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_15353_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_15353_row8_col2\" class=\"data row8 col2\" >60000.00</td>\n",
       "      <td id=\"T_15353_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row8_col4\" class=\"data row8 col4\" >1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_15353_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_15353_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_15353_row9_col2\" class=\"data row9 col2\" >40000.00</td>\n",
       "      <td id=\"T_15353_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_15353_row9_col4\" class=\"data row9 col4\" >1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_15353_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_15353_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_15353_row10_col2\" class=\"data row10 col2\" >30000.00</td>\n",
       "      <td id=\"T_15353_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row10_col4\" class=\"data row10 col4\" >1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_15353_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_15353_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_15353_row11_col2\" class=\"data row11 col2\" >30000.00</td>\n",
       "      <td id=\"T_15353_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_15353_row11_col4\" class=\"data row11 col4\" >4.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_15353_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_15353_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_15353_row12_col2\" class=\"data row12 col2\" >20000.00</td>\n",
       "      <td id=\"T_15353_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_15353_row12_col4\" class=\"data row12 col4\" >1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_15353_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_15353_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_15353_row13_col2\" class=\"data row13 col2\" >30000.00</td>\n",
       "      <td id=\"T_15353_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row13_col4\" class=\"data row13 col4\" >1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_15353_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_15353_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_15353_row14_col2\" class=\"data row14 col2\" >37500.00</td>\n",
       "      <td id=\"T_15353_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row14_col4\" class=\"data row14 col4\" >1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_15353_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_15353_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_15353_row15_col2\" class=\"data row15 col2\" >27500.00</td>\n",
       "      <td id=\"T_15353_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row15_col4\" class=\"data row15 col4\" >1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_15353_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_15353_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_15353_row16_col2\" class=\"data row16 col2\" >37500.00</td>\n",
       "      <td id=\"T_15353_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row16_col4\" class=\"data row16 col4\" >1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_15353_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_15353_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_15353_row17_col2\" class=\"data row17 col2\" >37500.00</td>\n",
       "      <td id=\"T_15353_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row17_col4\" class=\"data row17 col4\" >2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_15353_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_15353_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_15353_row18_col2\" class=\"data row18 col2\" >30000.00</td>\n",
       "      <td id=\"T_15353_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_15353_row18_col4\" class=\"data row18 col4\" >3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_15353_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_15353_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_15353_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_15353_row19_col2\" class=\"data row19 col2\" >37500.00</td>\n",
       "      <td id=\"T_15353_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_15353_row19_col4\" class=\"data row19 col4\" >0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b5cdf0a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.style.format('{:.2f}',subset=['salary','matchScore'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14 标记缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将缺失值标记为`数据缺失`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c8b01\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c8b01_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_c8b01_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_c8b01_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_c8b01_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_c8b01_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_c8b01_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_c8b01_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_c8b01_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_c8b01_row0_col3\" class=\"data row0 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_c8b01_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_c8b01_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_c8b01_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_c8b01_row1_col3\" class=\"data row1 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_c8b01_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_c8b01_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_c8b01_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_c8b01_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_c8b01_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_c8b01_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_c8b01_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_c8b01_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_c8b01_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_c8b01_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_c8b01_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_c8b01_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_c8b01_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_c8b01_row4_col3\" class=\"data row4 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_c8b01_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_c8b01_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_c8b01_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_c8b01_row5_col3\" class=\"data row5 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_c8b01_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_c8b01_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_c8b01_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_c8b01_row6_col3\" class=\"data row6 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_c8b01_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_c8b01_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_c8b01_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_c8b01_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_c8b01_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_c8b01_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_c8b01_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_c8b01_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_c8b01_row8_col3\" class=\"data row8 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_c8b01_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_c8b01_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_c8b01_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_c8b01_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_c8b01_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_c8b01_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_c8b01_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_c8b01_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_c8b01_row10_col3\" class=\"data row10 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_c8b01_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_c8b01_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_c8b01_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_c8b01_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_c8b01_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_c8b01_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_c8b01_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_c8b01_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_c8b01_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_c8b01_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_c8b01_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_c8b01_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_c8b01_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_c8b01_row13_col3\" class=\"data row13 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_c8b01_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_c8b01_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_c8b01_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_c8b01_row14_col3\" class=\"data row14 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_c8b01_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_c8b01_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_c8b01_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_c8b01_row15_col3\" class=\"data row15 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_c8b01_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_c8b01_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_c8b01_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_c8b01_row16_col3\" class=\"data row16 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_c8b01_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_c8b01_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_c8b01_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_c8b01_row17_col3\" class=\"data row17 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_c8b01_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_c8b01_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_c8b01_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_c8b01_row18_col3\" class=\"data row18 col3\" >数据缺失</td>\n",
       "      <td id=\"T_c8b01_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c8b01_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_c8b01_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_c8b01_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_c8b01_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_c8b01_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_c8b01_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b5e23fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.style.format(na_rep='数据缺失')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15 高亮缺失值\n",
    "\n",
    "<br>\n",
    "\n",
    "将缺失值高亮，颜色名`skyblue`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_53351_row0_col3, #T_53351_row1_col3, #T_53351_row4_col3, #T_53351_row5_col3, #T_53351_row6_col3, #T_53351_row8_col3, #T_53351_row10_col3, #T_53351_row13_col3, #T_53351_row14_col3, #T_53351_row15_col3, #T_53351_row16_col3, #T_53351_row17_col3, #T_53351_row18_col3 {\n",
       "  background-color: skyblue;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_53351\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_53351_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_53351_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_53351_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_53351_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_53351_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_53351_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_53351_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_53351_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_53351_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_53351_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_53351_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_53351_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_53351_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_53351_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_53351_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_53351_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_53351_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_53351_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_53351_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_53351_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_53351_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_53351_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_53351_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_53351_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_53351_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_53351_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_53351_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_53351_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_53351_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_53351_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_53351_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_53351_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_53351_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_53351_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_53351_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_53351_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_53351_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_53351_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_53351_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_53351_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_53351_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_53351_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_53351_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_53351_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_53351_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_53351_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_53351_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_53351_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_53351_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_53351_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_53351_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_53351_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_53351_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_53351_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_53351_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_53351_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_53351_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_53351_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_53351_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_53351_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_53351_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_53351_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_53351_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_53351_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_53351_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_53351_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_53351_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_53351_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_53351_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_53351_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_53351_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_53351_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_53351_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_53351_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_53351_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_53351_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_53351_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_53351_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_53351_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_53351_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_53351_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_53351_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_53351_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_53351_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_53351_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_53351_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_53351_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_53351_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_53351_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_53351_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_53351_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_53351_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_53351_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_53351_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b64449a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 注意版本\n",
    "data.style.highlight_null(color='skyblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面这个表格汇总了不同输出场景对样式的支持情况，方便您快速了解。\n",
    "\n",
    "| 输出场景 | 是否支持颜色样式 | 关键说明 |\n",
    "| :--- | :--- | :--- |\n",
    "| **Jupyter Notebook/Lab** | ✅ **完全支持** | 原生支持HTML/CSS渲染，是样式功能的主场。 |\n",
    "| **导出为HTML文件** | ✅ **完全支持** | 样式可完整保留，适合生成可分享的数据报告。 |\n",
    "| **导出为Excel文件** | ⚠️ **部分支持** | 仅支持通过`.apply()`/`.map()`等方法直接添加的样式，复杂的CSS样式可能丢失。 |\n",
    "| **控制台打印 (print)** | ❌ **不支持** | 只能输出纯文本，所有样式失效。 |\n",
    "| **其他IDE的控制台** | ❌ **通常不支持** | 如PyCharm、VSCode的终端，通常也不支持渲染HTML样式。 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16 高亮数值列最大值\n",
    "<br>\n",
    "将 数值格式列的最大值进行高亮"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_91a09_row1_col4, #T_91a09_row8_col2 {\n",
       "  background-color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_91a09\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_91a09_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_91a09_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_91a09_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_91a09_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_91a09_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_91a09_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_91a09_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_91a09_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_91a09_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_91a09_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_91a09_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_91a09_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_91a09_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_91a09_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_91a09_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_91a09_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_91a09_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_91a09_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_91a09_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_91a09_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_91a09_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_91a09_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_91a09_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_91a09_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_91a09_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_91a09_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_91a09_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_91a09_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_91a09_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_91a09_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_91a09_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_91a09_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_91a09_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_91a09_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_91a09_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_91a09_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_91a09_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_91a09_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_91a09_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_91a09_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_91a09_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_91a09_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_91a09_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_91a09_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_91a09_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_91a09_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_91a09_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_91a09_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_91a09_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_91a09_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_91a09_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_91a09_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_91a09_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_91a09_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_91a09_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_91a09_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_91a09_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_91a09_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_91a09_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_91a09_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_91a09_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_91a09_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_91a09_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_91a09_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_91a09_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_91a09_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_91a09_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_91a09_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_91a09_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_91a09_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_91a09_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_91a09_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_91a09_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_91a09_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_91a09_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_91a09_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_91a09_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_91a09_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_91a09_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_91a09_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_91a09_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_91a09_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_91a09_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_91a09_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_91a09_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_91a09_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_91a09_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_91a09_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a09_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_91a09_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_91a09_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_91a09_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_91a09_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_91a09_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b96bde40>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.style.highlight_max(color='green',subset=['salary','matchScore'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17 高亮数值列最小值\n",
    "<br>\n",
    "将 数值格式列的最小值进行高亮"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_48a18_row2_col2, #T_48a18_row19_col4 {\n",
       "  background-color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_48a18\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_48a18_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_48a18_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_48a18_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_48a18_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_48a18_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_48a18_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_48a18_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_48a18_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_48a18_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_48a18_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_48a18_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_48a18_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_48a18_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_48a18_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_48a18_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_48a18_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_48a18_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_48a18_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_48a18_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_48a18_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_48a18_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_48a18_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_48a18_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_48a18_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_48a18_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_48a18_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_48a18_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_48a18_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_48a18_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_48a18_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_48a18_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_48a18_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_48a18_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_48a18_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_48a18_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_48a18_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_48a18_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_48a18_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_48a18_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_48a18_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_48a18_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_48a18_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_48a18_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_48a18_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_48a18_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_48a18_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_48a18_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_48a18_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_48a18_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_48a18_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_48a18_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_48a18_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_48a18_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_48a18_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_48a18_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_48a18_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_48a18_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_48a18_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_48a18_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_48a18_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_48a18_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_48a18_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_48a18_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_48a18_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_48a18_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_48a18_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_48a18_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_48a18_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_48a18_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_48a18_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_48a18_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_48a18_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_48a18_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_48a18_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_48a18_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_48a18_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_48a18_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_48a18_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_48a18_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_48a18_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_48a18_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_48a18_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_48a18_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_48a18_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_48a18_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_48a18_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_48a18_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_48a18_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_48a18_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_48a18_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_48a18_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_48a18_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_48a18_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_48a18_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b96bc940>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.style.highlight_min(color='red',subset=['salary','matchScore'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18 同时高亮最大最小值\n",
    "\n",
    "<br>\n",
    "\n",
    "同时高亮最大值（颜色代码为`#F77802`）与最小值（颜色代码为`#26BE49`）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6239e_row1_col4, #T_6239e_row8_col2 {\n",
       "  background-color: #F77802;\n",
       "}\n",
       "#T_6239e_row2_col2, #T_6239e_row19_col4 {\n",
       "  background-color: #26BE49;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6239e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6239e_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_6239e_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_6239e_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_6239e_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_6239e_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_6239e_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_6239e_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_6239e_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_6239e_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_6239e_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_6239e_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_6239e_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_6239e_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_6239e_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_6239e_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_6239e_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_6239e_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_6239e_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_6239e_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_6239e_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_6239e_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_6239e_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_6239e_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_6239e_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_6239e_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_6239e_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_6239e_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_6239e_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_6239e_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_6239e_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_6239e_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_6239e_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_6239e_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_6239e_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_6239e_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_6239e_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_6239e_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_6239e_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_6239e_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_6239e_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_6239e_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_6239e_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_6239e_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_6239e_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_6239e_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_6239e_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_6239e_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_6239e_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_6239e_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_6239e_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_6239e_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_6239e_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_6239e_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_6239e_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_6239e_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_6239e_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_6239e_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_6239e_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_6239e_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_6239e_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_6239e_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_6239e_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_6239e_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_6239e_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_6239e_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_6239e_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_6239e_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_6239e_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_6239e_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_6239e_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_6239e_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_6239e_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_6239e_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_6239e_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_6239e_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_6239e_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_6239e_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_6239e_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_6239e_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_6239e_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_6239e_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_6239e_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_6239e_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_6239e_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_6239e_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_6239e_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_6239e_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_6239e_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6239e_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_6239e_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_6239e_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_6239e_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_6239e_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_6239e_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b96bd390>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 链式调用\n",
    "data.style.highlight_max(color='#F77802',\n",
    "                         subset=['salary','matchScore']\n",
    "                         ).highlight_min(color='#26BE49',\n",
    "                                         subset=['salary','matchScore'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19 指定格式高亮"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "高亮 `salary` 列范围在 3000 - 10000 的数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_02159_row2_col2 {\n",
       "  background-color: #66ccff;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_02159\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_02159_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_02159_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_02159_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_02159_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_02159_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_02159_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_02159_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_02159_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_02159_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_02159_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_02159_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_02159_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_02159_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_02159_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_02159_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_02159_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_02159_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_02159_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_02159_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_02159_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_02159_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_02159_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_02159_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_02159_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_02159_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_02159_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_02159_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_02159_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_02159_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_02159_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_02159_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_02159_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_02159_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_02159_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_02159_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_02159_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_02159_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_02159_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_02159_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_02159_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_02159_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_02159_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_02159_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_02159_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_02159_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_02159_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_02159_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_02159_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_02159_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_02159_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_02159_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_02159_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_02159_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_02159_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_02159_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_02159_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_02159_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_02159_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_02159_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_02159_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_02159_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_02159_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_02159_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_02159_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_02159_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_02159_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_02159_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_02159_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_02159_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_02159_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_02159_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_02159_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_02159_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_02159_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_02159_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_02159_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_02159_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_02159_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_02159_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_02159_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_02159_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_02159_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_02159_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_02159_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_02159_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_02159_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_02159_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_02159_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02159_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_02159_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_02159_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_02159_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_02159_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_02159_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b96bed40>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.style.highlight_between(color='#66ccff',subset=['salary'],left=3000,right=10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20 渐变显示数值列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将数值格式的列使用渐变色（绿色）进行显示，以突出趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_29028_row0_col2, #T_29028_row14_col2, #T_29028_row16_col2, #T_29028_row17_col2, #T_29028_row19_col2 {\n",
       "  background-color: #4aaf61;\n",
       "  color: #f1f1f1;\n",
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       "#T_29028_row0_col4 {\n",
       "  background-color: #86cc85;\n",
       "  color: #000000;\n",
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       "#T_29028_row1_col2 {\n",
       "  background-color: #d2edcc;\n",
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       "#T_29028_row1_col4, #T_29028_row8_col2 {\n",
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       "  background-color: #9fd899;\n",
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       "#T_29028_row4_col2, #T_29028_row6_col2, #T_29028_row10_col2, #T_29028_row11_col2, #T_29028_row13_col2, #T_29028_row18_col2 {\n",
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       "  background-color: #bbe4b4;\n",
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       "  background-color: #90d18d;\n",
       "  color: #000000;\n",
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       "</style>\n",
       "<table id=\"T_29028\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_29028_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_29028_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_29028_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_29028_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_29028_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_29028_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_29028_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_29028_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_29028_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_29028_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_29028_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_29028_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_29028_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_29028_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_29028_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_29028_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_29028_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_29028_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_29028_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_29028_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_29028_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_29028_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_29028_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_29028_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_29028_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_29028_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_29028_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_29028_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_29028_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_29028_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_29028_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_29028_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_29028_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_29028_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_29028_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_29028_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_29028_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_29028_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_29028_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_29028_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_29028_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_29028_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_29028_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_29028_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_29028_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_29028_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_29028_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_29028_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_29028_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_29028_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_29028_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_29028_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_29028_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_29028_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_29028_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_29028_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_29028_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_29028_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_29028_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_29028_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_29028_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_29028_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_29028_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_29028_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_29028_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_29028_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_29028_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_29028_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_29028_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_29028_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_29028_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_29028_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_29028_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_29028_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_29028_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_29028_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_29028_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_29028_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_29028_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_29028_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_29028_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_29028_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_29028_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_29028_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_29028_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_29028_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_29028_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_29028_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29028_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_29028_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_29028_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_29028_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_29028_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_29028_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b96bd930>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.style.background_gradient(cmap='Greens',subset=['salary','matchScore'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 21 修改字体颜色\n",
    "\n",
    "<br>\n",
    "\n",
    "将 `salary` 列修改为红色字体"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Windows\\Temp\\ipykernel_19784\\570377696.py:1: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead.\n",
      "  data.style.applymap(lambda x:'color:red',subset=['salary'])\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9de19_row0_col2, #T_9de19_row1_col2, #T_9de19_row2_col2, #T_9de19_row3_col2, #T_9de19_row4_col2, #T_9de19_row5_col2, #T_9de19_row6_col2, #T_9de19_row7_col2, #T_9de19_row8_col2, #T_9de19_row9_col2, #T_9de19_row10_col2, #T_9de19_row11_col2, #T_9de19_row12_col2, #T_9de19_row13_col2, #T_9de19_row14_col2, #T_9de19_row15_col2, #T_9de19_row16_col2, #T_9de19_row17_col2, #T_9de19_row18_col2, #T_9de19_row19_col2 {\n",
       "  color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9de19\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9de19_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_9de19_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_9de19_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_9de19_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_9de19_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_9de19_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_9de19_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_9de19_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_9de19_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_9de19_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_9de19_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_9de19_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_9de19_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_9de19_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_9de19_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_9de19_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_9de19_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_9de19_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_9de19_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_9de19_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_9de19_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_9de19_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_9de19_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_9de19_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_9de19_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_9de19_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_9de19_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_9de19_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_9de19_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_9de19_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_9de19_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_9de19_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_9de19_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_9de19_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_9de19_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_9de19_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_9de19_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_9de19_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_9de19_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_9de19_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_9de19_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_9de19_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_9de19_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_9de19_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_9de19_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_9de19_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_9de19_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_9de19_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_9de19_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_9de19_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_9de19_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_9de19_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_9de19_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_9de19_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_9de19_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_9de19_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_9de19_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_9de19_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_9de19_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_9de19_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_9de19_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_9de19_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_9de19_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_9de19_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_9de19_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_9de19_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_9de19_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_9de19_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_9de19_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_9de19_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_9de19_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_9de19_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_9de19_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_9de19_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_9de19_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_9de19_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_9de19_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_9de19_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_9de19_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_9de19_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_9de19_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_9de19_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_9de19_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_9de19_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_9de19_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_9de19_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_9de19_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_9de19_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9de19_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_9de19_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_9de19_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_9de19_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_9de19_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_9de19_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b64673a0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.style.applymap(lambda x:'color:red',subset=['salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 22 修改背景颜色、对齐方式、字体大小\n",
    "<br>\n",
    "\n",
    "将整个 `dataframe` 进行如下设置：\n",
    "- 居中\n",
    "- 背景色修改为 `#F8F8FF`\n",
    "- 字体:13px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7c62d_row0_col0, #T_7c62d_row0_col1, #T_7c62d_row0_col2, #T_7c62d_row0_col3, #T_7c62d_row0_col4, #T_7c62d_row1_col0, #T_7c62d_row1_col1, #T_7c62d_row1_col2, #T_7c62d_row1_col3, #T_7c62d_row1_col4, #T_7c62d_row2_col0, #T_7c62d_row2_col1, #T_7c62d_row2_col2, #T_7c62d_row2_col3, #T_7c62d_row2_col4, #T_7c62d_row3_col0, #T_7c62d_row3_col1, #T_7c62d_row3_col2, #T_7c62d_row3_col3, #T_7c62d_row3_col4, #T_7c62d_row4_col0, #T_7c62d_row4_col1, #T_7c62d_row4_col2, #T_7c62d_row4_col3, #T_7c62d_row4_col4, #T_7c62d_row5_col0, #T_7c62d_row5_col1, #T_7c62d_row5_col2, #T_7c62d_row5_col3, #T_7c62d_row5_col4, #T_7c62d_row6_col0, #T_7c62d_row6_col1, #T_7c62d_row6_col2, #T_7c62d_row6_col3, #T_7c62d_row6_col4, #T_7c62d_row7_col0, #T_7c62d_row7_col1, #T_7c62d_row7_col2, #T_7c62d_row7_col3, #T_7c62d_row7_col4, #T_7c62d_row8_col0, #T_7c62d_row8_col1, #T_7c62d_row8_col2, #T_7c62d_row8_col3, #T_7c62d_row8_col4, #T_7c62d_row9_col0, #T_7c62d_row9_col1, #T_7c62d_row9_col2, #T_7c62d_row9_col3, #T_7c62d_row9_col4, #T_7c62d_row10_col0, #T_7c62d_row10_col1, #T_7c62d_row10_col2, #T_7c62d_row10_col3, #T_7c62d_row10_col4, #T_7c62d_row11_col0, #T_7c62d_row11_col1, #T_7c62d_row11_col2, #T_7c62d_row11_col3, #T_7c62d_row11_col4, #T_7c62d_row12_col0, #T_7c62d_row12_col1, #T_7c62d_row12_col2, #T_7c62d_row12_col3, #T_7c62d_row12_col4, #T_7c62d_row13_col0, #T_7c62d_row13_col1, #T_7c62d_row13_col2, #T_7c62d_row13_col3, #T_7c62d_row13_col4, #T_7c62d_row14_col0, #T_7c62d_row14_col1, #T_7c62d_row14_col2, #T_7c62d_row14_col3, #T_7c62d_row14_col4, #T_7c62d_row15_col0, #T_7c62d_row15_col1, #T_7c62d_row15_col2, #T_7c62d_row15_col3, #T_7c62d_row15_col4, #T_7c62d_row16_col0, #T_7c62d_row16_col1, #T_7c62d_row16_col2, #T_7c62d_row16_col3, #T_7c62d_row16_col4, #T_7c62d_row17_col0, #T_7c62d_row17_col1, #T_7c62d_row17_col2, #T_7c62d_row17_col3, #T_7c62d_row17_col4, #T_7c62d_row18_col0, #T_7c62d_row18_col1, #T_7c62d_row18_col2, #T_7c62d_row18_col3, #T_7c62d_row18_col4, #T_7c62d_row19_col0, #T_7c62d_row19_col1, #T_7c62d_row19_col2, #T_7c62d_row19_col3, #T_7c62d_row19_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #F8F8FF;\n",
       "  color: black;\n",
       "  font-size: 13px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7c62d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7c62d_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_7c62d_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_7c62d_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_7c62d_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_7c62d_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7c62d_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_7c62d_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_7c62d_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_7c62d_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7c62d_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_7c62d_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_7c62d_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_7c62d_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_7c62d_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_7c62d_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_7c62d_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_7c62d_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_7c62d_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_7c62d_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_7c62d_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_7c62d_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_7c62d_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_7c62d_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7c62d_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_7c62d_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_7c62d_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_7c62d_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7c62d_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_7c62d_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_7c62d_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_7c62d_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_7c62d_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_7c62d_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_7c62d_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_7c62d_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_7c62d_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_7c62d_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_7c62d_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_7c62d_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_7c62d_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7c62d_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_7c62d_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_7c62d_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_7c62d_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7c62d_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_7c62d_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_7c62d_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_7c62d_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_7c62d_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_7c62d_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_7c62d_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_7c62d_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_7c62d_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_7c62d_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_7c62d_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_7c62d_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_7c62d_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_7c62d_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_7c62d_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_7c62d_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_7c62d_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_7c62d_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_7c62d_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_7c62d_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_7c62d_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_7c62d_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_7c62d_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_7c62d_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_7c62d_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_7c62d_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_7c62d_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_7c62d_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_7c62d_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_7c62d_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_7c62d_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_7c62d_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_7c62d_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_7c62d_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_7c62d_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_7c62d_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_7c62d_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_7c62d_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_7c62d_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_7c62d_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_7c62d_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_7c62d_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_7c62d_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_7c62d_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c62d_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_7c62d_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_7c62d_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_7c62d_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_7c62d_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_7c62d_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b6465000>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    data.style\n",
    "    .set_properties(**{\n",
    "        'text-align': 'center',      # 内容居中\n",
    "        'background-color': \"#F8F8FF\", # 背景颜色\n",
    "        'color':'black',\n",
    "        'font-size': '13px'           # 字体大小\n",
    "    })\n",
    "    # .set_table_styles([\n",
    "    #     # 设置表头样式：加粗，白色字体，深色背景\n",
    "    #     {\n",
    "    #         'selector': 'th',\n",
    "    #         'props': [\n",
    "    #             ('font-weight', 'bold'),\n",
    "    #             ('color', '#66ccff'),\n",
    "    #             ('background-color', '#4A4AFF') # 例如一个深蓝色背景\n",
    "    #         ]\n",
    "    #     }\n",
    "    # ], overwrite=False)  # 重要：overwrite=False 以保留之前通过.set_properties设置的全局样式\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 23 综合(链式)设置\n",
    "<br>\n",
    "\n",
    "除了上面的单个设置，还可以将多个设置进行结合，下面对整个 `dataframe` 进行如下设置：\n",
    "- 居中\n",
    "- 背景色修改为 `#F8F8FF`\n",
    "- 字体:13px\n",
    "\n",
    "并将 `salary` 列字体修改为红色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_098f2_row0_col0, #T_098f2_row0_col1, #T_098f2_row0_col3, #T_098f2_row0_col4, #T_098f2_row1_col0, #T_098f2_row1_col1, #T_098f2_row1_col3, #T_098f2_row1_col4, #T_098f2_row2_col0, #T_098f2_row2_col1, #T_098f2_row2_col3, #T_098f2_row2_col4, #T_098f2_row3_col0, #T_098f2_row3_col1, #T_098f2_row3_col3, #T_098f2_row3_col4, #T_098f2_row4_col0, #T_098f2_row4_col1, #T_098f2_row4_col3, #T_098f2_row4_col4, #T_098f2_row5_col0, #T_098f2_row5_col1, #T_098f2_row5_col3, #T_098f2_row5_col4, #T_098f2_row6_col0, #T_098f2_row6_col1, #T_098f2_row6_col3, #T_098f2_row6_col4, #T_098f2_row7_col0, #T_098f2_row7_col1, #T_098f2_row7_col3, #T_098f2_row7_col4, #T_098f2_row8_col0, #T_098f2_row8_col1, #T_098f2_row8_col3, #T_098f2_row8_col4, #T_098f2_row9_col0, #T_098f2_row9_col1, #T_098f2_row9_col3, #T_098f2_row9_col4, #T_098f2_row10_col0, #T_098f2_row10_col1, #T_098f2_row10_col3, #T_098f2_row10_col4, #T_098f2_row11_col0, #T_098f2_row11_col1, #T_098f2_row11_col3, #T_098f2_row11_col4, #T_098f2_row12_col0, #T_098f2_row12_col1, #T_098f2_row12_col3, #T_098f2_row12_col4, #T_098f2_row13_col0, #T_098f2_row13_col1, #T_098f2_row13_col3, #T_098f2_row13_col4, #T_098f2_row14_col0, #T_098f2_row14_col1, #T_098f2_row14_col3, #T_098f2_row14_col4, #T_098f2_row15_col0, #T_098f2_row15_col1, #T_098f2_row15_col3, #T_098f2_row15_col4, #T_098f2_row16_col0, #T_098f2_row16_col1, #T_098f2_row16_col3, #T_098f2_row16_col4, #T_098f2_row17_col0, #T_098f2_row17_col1, #T_098f2_row17_col3, #T_098f2_row17_col4, #T_098f2_row18_col0, #T_098f2_row18_col1, #T_098f2_row18_col3, #T_098f2_row18_col4, #T_098f2_row19_col0, #T_098f2_row19_col1, #T_098f2_row19_col3, #T_098f2_row19_col4 {\n",
       "  text-align: center;\n",
       "  background-color: #F8F8FF;\n",
       "  color: black;\n",
       "  font-size: 13px;\n",
       "}\n",
       "#T_098f2_row0_col2, #T_098f2_row1_col2, #T_098f2_row2_col2, #T_098f2_row3_col2, #T_098f2_row4_col2, #T_098f2_row5_col2, #T_098f2_row6_col2, #T_098f2_row7_col2, #T_098f2_row8_col2, #T_098f2_row9_col2, #T_098f2_row10_col2, #T_098f2_row11_col2, #T_098f2_row12_col2, #T_098f2_row13_col2, #T_098f2_row14_col2, #T_098f2_row15_col2, #T_098f2_row16_col2, #T_098f2_row17_col2, #T_098f2_row18_col2, #T_098f2_row19_col2 {\n",
       "  text-align: center;\n",
       "  background-color: #F8F8FF;\n",
       "  color: black;\n",
       "  font-size: 13px;\n",
       "  color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_098f2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_098f2_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_098f2_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_098f2_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_098f2_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_098f2_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_098f2_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_098f2_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_098f2_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_098f2_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_098f2_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_098f2_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_098f2_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_098f2_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_098f2_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_098f2_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_098f2_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_098f2_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_098f2_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_098f2_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_098f2_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_098f2_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_098f2_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_098f2_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_098f2_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_098f2_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_098f2_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_098f2_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_098f2_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_098f2_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_098f2_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_098f2_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_098f2_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_098f2_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_098f2_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_098f2_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_098f2_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_098f2_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_098f2_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_098f2_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_098f2_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_098f2_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_098f2_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_098f2_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_098f2_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_098f2_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_098f2_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_098f2_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_098f2_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_098f2_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_098f2_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_098f2_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_098f2_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_098f2_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_098f2_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_098f2_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_098f2_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_098f2_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_098f2_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_098f2_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_098f2_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_098f2_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_098f2_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_098f2_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_098f2_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_098f2_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_098f2_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_098f2_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_098f2_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_098f2_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_098f2_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_098f2_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_098f2_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_098f2_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_098f2_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_098f2_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_098f2_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_098f2_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_098f2_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_098f2_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_098f2_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_098f2_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_098f2_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_098f2_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_098f2_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_098f2_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_098f2_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_098f2_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_098f2_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_098f2_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_098f2_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_098f2_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_098f2_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_098f2_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_098f2_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b97ee650>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    data.style\n",
    "    .set_properties(**{\n",
    "        'text-align': 'center',      # 内容居中\n",
    "        'background-color': \"#F8F8FF\", # 背景颜色\n",
    "        'color':'black',\n",
    "        'font-size': '13px'           # 字体大小\n",
    "    })\n",
    "    .map(lambda x:'color:red',subset=['salary'])\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24 导出样式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将上一题带有样式的 `pandas` 数据框导出为本地 Excel(`.xlsx`格式)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先创建output文件夹\n",
    "(\n",
    "    data.style\n",
    "    .set_properties(**{\n",
    "        'text-align': 'center',      # 内容居中\n",
    "        'background-color': \"#2DAF84\", # 背景颜色\n",
    "        'color':'black',\n",
    "        'font-size': '13px'           # 字体大小\n",
    "    })\n",
    "    .map(lambda x:'color:red',subset=['salary'])\n",
    "    .to_excel('output/data24.xlsx')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 25 制作指定列条形图\n",
    "\n",
    "<br>\n",
    "\n",
    "在 `pandas` 中对 `salary` 列使用条形图进行可视化，指定颜色`skyblue`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 绘制条形图，并指定颜色为'skyblue'\n",
    "data.plot(kind='bar', x='positionName', y='salary', color='skyblue')\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # Windows 系统常用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号('-')显示为方块的问题\n",
    "plt.title('Salary Distribution') # 添加标题\n",
    "plt.xlabel('Position Name') # 设置x轴标签\n",
    "plt.ylabel('Salary') # 设置y轴标签\n",
    "plt.show() # 显示图表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 26 带有条件的样式（自定义样式）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将 `salary` 列数值大于 30000 的单元格字体修改为红色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_618cc_row0_col2, #T_618cc_row3_col2, #T_618cc_row5_col2, #T_618cc_row7_col2, #T_618cc_row8_col2, #T_618cc_row9_col2, #T_618cc_row14_col2, #T_618cc_row16_col2, #T_618cc_row17_col2, #T_618cc_row19_col2 {\n",
       "  color: red;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_618cc\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_618cc_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_618cc_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_618cc_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_618cc_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_618cc_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_618cc_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_618cc_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_618cc_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_618cc_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_618cc_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_618cc_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_618cc_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_618cc_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_618cc_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_618cc_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_618cc_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_618cc_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_618cc_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_618cc_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_618cc_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_618cc_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_618cc_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_618cc_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_618cc_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_618cc_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_618cc_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_618cc_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_618cc_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_618cc_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_618cc_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_618cc_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_618cc_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_618cc_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_618cc_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_618cc_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_618cc_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_618cc_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_618cc_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_618cc_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_618cc_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_618cc_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_618cc_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_618cc_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_618cc_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_618cc_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_618cc_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_618cc_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_618cc_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_618cc_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_618cc_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_618cc_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_618cc_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_618cc_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_618cc_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_618cc_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_618cc_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_618cc_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_618cc_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_618cc_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_618cc_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_618cc_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_618cc_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_618cc_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_618cc_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_618cc_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_618cc_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_618cc_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_618cc_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_618cc_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_618cc_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_618cc_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_618cc_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_618cc_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_618cc_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_618cc_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_618cc_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_618cc_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_618cc_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_618cc_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_618cc_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_618cc_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_618cc_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_618cc_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_618cc_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_618cc_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_618cc_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_618cc_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_618cc_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_618cc_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_618cc_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_618cc_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_618cc_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_618cc_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_618cc_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156bc4e8a30>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def set_color_condition(salary):\n",
    "    if salary>30000:\n",
    "        return 'color:red'\n",
    "    else:\n",
    "        return ''\n",
    "data.style.map(set_color_condition,subset=['salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 27 格式化输出日期类型\n",
    "\n",
    "<br>\n",
    "\n",
    "将 `createTime` 列格式化输出为 `xx年xx月xx日` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2569d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2569d_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_2569d_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_2569d_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_2569d_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_2569d_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_2569d_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_2569d_row0_col1\" class=\"data row0 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row0_col2\" class=\"data row0 col2\" >37500</td>\n",
       "      <td id=\"T_2569d_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row0_col4\" class=\"data row0 col4\" >15.101875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_2569d_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_2569d_row1_col1\" class=\"data row1 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row1_col2\" class=\"data row1 col2\" >15000</td>\n",
       "      <td id=\"T_2569d_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row1_col4\" class=\"data row1 col4\" >32.559414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_2569d_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_2569d_row2_col1\" class=\"data row2 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row2_col2\" class=\"data row2 col2\" >3500</td>\n",
       "      <td id=\"T_2569d_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_2569d_row2_col4\" class=\"data row2 col4\" >14.972357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_2569d_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_2569d_row3_col1\" class=\"data row3 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row3_col2\" class=\"data row3 col2\" >45000</td>\n",
       "      <td id=\"T_2569d_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_2569d_row3_col4\" class=\"data row3 col4\" >12.874153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_2569d_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_2569d_row4_col1\" class=\"data row4 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row4_col2\" class=\"data row4 col2\" >30000</td>\n",
       "      <td id=\"T_2569d_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row4_col4\" class=\"data row4 col4\" >12.755375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_2569d_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_2569d_row5_col1\" class=\"data row5 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row5_col2\" class=\"data row5 col2\" >50000</td>\n",
       "      <td id=\"T_2569d_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row5_col4\" class=\"data row5 col4\" >12.718732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_2569d_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_2569d_row6_col1\" class=\"data row6 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row6_col2\" class=\"data row6 col2\" >30000</td>\n",
       "      <td id=\"T_2569d_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row6_col4\" class=\"data row6 col4\" >12.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_2569d_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_2569d_row7_col1\" class=\"data row7 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row7_col2\" class=\"data row7 col2\" >35000</td>\n",
       "      <td id=\"T_2569d_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_2569d_row7_col4\" class=\"data row7 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_2569d_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_2569d_row8_col1\" class=\"data row8 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row8_col2\" class=\"data row8 col2\" >60000</td>\n",
       "      <td id=\"T_2569d_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row8_col4\" class=\"data row8 col4\" >1.141952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_2569d_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2569d_row9_col1\" class=\"data row9 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row9_col2\" class=\"data row9 col2\" >40000</td>\n",
       "      <td id=\"T_2569d_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_2569d_row9_col4\" class=\"data row9 col4\" >1.177361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_2569d_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2569d_row10_col1\" class=\"data row10 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row10_col2\" class=\"data row10 col2\" >30000</td>\n",
       "      <td id=\"T_2569d_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row10_col4\" class=\"data row10 col4\" >1.161869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_2569d_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_2569d_row11_col1\" class=\"data row11 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row11_col2\" class=\"data row11 col2\" >30000</td>\n",
       "      <td id=\"T_2569d_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_2569d_row11_col4\" class=\"data row11 col4\" >4.245066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_2569d_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_2569d_row12_col1\" class=\"data row12 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row12_col2\" class=\"data row12 col2\" >20000</td>\n",
       "      <td id=\"T_2569d_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_2569d_row12_col4\" class=\"data row12 col4\" >1.091051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_2569d_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_2569d_row13_col1\" class=\"data row13 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row13_col2\" class=\"data row13 col2\" >30000</td>\n",
       "      <td id=\"T_2569d_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row13_col4\" class=\"data row13 col4\" >1.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_2569d_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2569d_row14_col1\" class=\"data row14 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row14_col2\" class=\"data row14 col2\" >37500</td>\n",
       "      <td id=\"T_2569d_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row14_col4\" class=\"data row14 col4\" >1.053428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_2569d_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_2569d_row15_col1\" class=\"data row15 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row15_col2\" class=\"data row15 col2\" >27500</td>\n",
       "      <td id=\"T_2569d_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row15_col4\" class=\"data row15 col4\" >1.015806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_2569d_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_2569d_row16_col1\" class=\"data row16 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row16_col2\" class=\"data row16 col2\" >37500</td>\n",
       "      <td id=\"T_2569d_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row16_col4\" class=\"data row16 col4\" >1.009167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_2569d_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_2569d_row17_col1\" class=\"data row17 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row17_col2\" class=\"data row17 col2\" >37500</td>\n",
       "      <td id=\"T_2569d_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row17_col4\" class=\"data row17 col4\" >2.719454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_2569d_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_2569d_row18_col1\" class=\"data row18 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row18_col2\" class=\"data row18 col2\" >30000</td>\n",
       "      <td id=\"T_2569d_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_2569d_row18_col4\" class=\"data row18 col4\" >3.033237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2569d_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_2569d_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_2569d_row19_col1\" class=\"data row19 col1\" >2020年03月16日</td>\n",
       "      <td id=\"T_2569d_row19_col2\" class=\"data row19 col2\" >37500</td>\n",
       "      <td id=\"T_2569d_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_2569d_row19_col4\" class=\"data row19 col4\" >0.834333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x156b91973d0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_dict = {\n",
    "    'createTime': '{:%Y年%m月%d日}'  # 这将把日期显示为“2023年05月15日”的格式\n",
    "}\n",
    "data.style.format(format_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 28 指定（自定义）格式化数据\n",
    "\n",
    "<br>\n",
    "\n",
    "- 在 `salary` 列后增加\"元\"\n",
    "- 对 `matchScore` 列保留两位小数并增加\"分\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2bef4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_2bef4_level0_col0\" class=\"col_heading level0 col0\" >positionName</th>\n",
       "      <th id=\"T_2bef4_level0_col1\" class=\"col_heading level0 col1\" >createTime</th>\n",
       "      <th id=\"T_2bef4_level0_col2\" class=\"col_heading level0 col2\" >salary</th>\n",
       "      <th id=\"T_2bef4_level0_col3\" class=\"col_heading level0 col3\" >subwayline</th>\n",
       "      <th id=\"T_2bef4_level0_col4\" class=\"col_heading level0 col4\" >matchScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_2bef4_row0_col0\" class=\"data row0 col0\" >数据分析</td>\n",
       "      <td id=\"T_2bef4_row0_col1\" class=\"data row0 col1\" >2020-03-16 11:00:00</td>\n",
       "      <td id=\"T_2bef4_row0_col2\" class=\"data row0 col2\" >37500元</td>\n",
       "      <td id=\"T_2bef4_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row0_col4\" class=\"data row0 col4\" >15.10分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_2bef4_row1_col0\" class=\"data row1 col0\" >数据建模</td>\n",
       "      <td id=\"T_2bef4_row1_col1\" class=\"data row1 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_2bef4_row1_col2\" class=\"data row1 col2\" >15000元</td>\n",
       "      <td id=\"T_2bef4_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row1_col4\" class=\"data row1 col4\" >32.56分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_2bef4_row2_col0\" class=\"data row2 col0\" >数据分析</td>\n",
       "      <td id=\"T_2bef4_row2_col1\" class=\"data row2 col1\" >2020-03-16 10:33:00</td>\n",
       "      <td id=\"T_2bef4_row2_col2\" class=\"data row2 col2\" >3500元</td>\n",
       "      <td id=\"T_2bef4_row2_col3\" class=\"data row2 col3\" >4号线</td>\n",
       "      <td id=\"T_2bef4_row2_col4\" class=\"data row2 col4\" >14.97分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_2bef4_row3_col0\" class=\"data row3 col0\" >数据分析</td>\n",
       "      <td id=\"T_2bef4_row3_col1\" class=\"data row3 col1\" >2020-03-16 10:10:00</td>\n",
       "      <td id=\"T_2bef4_row3_col2\" class=\"data row3 col2\" >45000元</td>\n",
       "      <td id=\"T_2bef4_row3_col3\" class=\"data row3 col3\" >1号线</td>\n",
       "      <td id=\"T_2bef4_row3_col4\" class=\"data row3 col4\" >12.87分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_2bef4_row4_col0\" class=\"data row4 col0\" >数据分析</td>\n",
       "      <td id=\"T_2bef4_row4_col1\" class=\"data row4 col1\" >2020-03-16 09:56:00</td>\n",
       "      <td id=\"T_2bef4_row4_col2\" class=\"data row4 col2\" >30000元</td>\n",
       "      <td id=\"T_2bef4_row4_col3\" class=\"data row4 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row4_col4\" class=\"data row4 col4\" >12.76分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_2bef4_row5_col0\" class=\"data row5 col0\" >数据分析</td>\n",
       "      <td id=\"T_2bef4_row5_col1\" class=\"data row5 col1\" >2020-03-16 09:54:00</td>\n",
       "      <td id=\"T_2bef4_row5_col2\" class=\"data row5 col2\" >50000元</td>\n",
       "      <td id=\"T_2bef4_row5_col3\" class=\"data row5 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row5_col4\" class=\"data row5 col4\" >12.72分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_2bef4_row6_col0\" class=\"data row6 col0\" >数据分析</td>\n",
       "      <td id=\"T_2bef4_row6_col1\" class=\"data row6 col1\" >2020-03-16 09:41:00</td>\n",
       "      <td id=\"T_2bef4_row6_col2\" class=\"data row6 col2\" >30000元</td>\n",
       "      <td id=\"T_2bef4_row6_col3\" class=\"data row6 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row6_col4\" class=\"data row6 col4\" >12.62分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_2bef4_row7_col0\" class=\"data row7 col0\" >数据建模工程师</td>\n",
       "      <td id=\"T_2bef4_row7_col1\" class=\"data row7 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_2bef4_row7_col2\" class=\"data row7 col2\" >35000元</td>\n",
       "      <td id=\"T_2bef4_row7_col3\" class=\"data row7 col3\" >2号线</td>\n",
       "      <td id=\"T_2bef4_row7_col4\" class=\"data row7 col4\" >3.03分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_2bef4_row8_col0\" class=\"data row8 col0\" >数据分析专家</td>\n",
       "      <td id=\"T_2bef4_row8_col1\" class=\"data row8 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_2bef4_row8_col2\" class=\"data row8 col2\" >60000元</td>\n",
       "      <td id=\"T_2bef4_row8_col3\" class=\"data row8 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row8_col4\" class=\"data row8 col4\" >1.14分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_2bef4_row9_col0\" class=\"data row9 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2bef4_row9_col1\" class=\"data row9 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_2bef4_row9_col2\" class=\"data row9 col2\" >40000元</td>\n",
       "      <td id=\"T_2bef4_row9_col3\" class=\"data row9 col3\" >2号线</td>\n",
       "      <td id=\"T_2bef4_row9_col4\" class=\"data row9 col4\" >1.18分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_2bef4_row10_col0\" class=\"data row10 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2bef4_row10_col1\" class=\"data row10 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_2bef4_row10_col2\" class=\"data row10 col2\" >30000元</td>\n",
       "      <td id=\"T_2bef4_row10_col3\" class=\"data row10 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row10_col4\" class=\"data row10 col4\" >1.16分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_2bef4_row11_col0\" class=\"data row11 col0\" >大数据分析工程师(J11108)</td>\n",
       "      <td id=\"T_2bef4_row11_col1\" class=\"data row11 col1\" >2020-03-16 09:25:00</td>\n",
       "      <td id=\"T_2bef4_row11_col2\" class=\"data row11 col2\" >30000元</td>\n",
       "      <td id=\"T_2bef4_row11_col3\" class=\"data row11 col3\" >2号线</td>\n",
       "      <td id=\"T_2bef4_row11_col4\" class=\"data row11 col4\" >4.25分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_2bef4_row12_col0\" class=\"data row12 col0\" >数据分析工程师</td>\n",
       "      <td id=\"T_2bef4_row12_col1\" class=\"data row12 col1\" >2020-03-16 11:18:00</td>\n",
       "      <td id=\"T_2bef4_row12_col2\" class=\"data row12 col2\" >20000元</td>\n",
       "      <td id=\"T_2bef4_row12_col3\" class=\"data row12 col3\" >2号线</td>\n",
       "      <td id=\"T_2bef4_row12_col4\" class=\"data row12 col4\" >1.09分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_2bef4_row13_col0\" class=\"data row13 col0\" >资深数据分析师</td>\n",
       "      <td id=\"T_2bef4_row13_col1\" class=\"data row13 col1\" >2020-03-16 10:57:00</td>\n",
       "      <td id=\"T_2bef4_row13_col2\" class=\"data row13 col2\" >30000元</td>\n",
       "      <td id=\"T_2bef4_row13_col3\" class=\"data row13 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row13_col4\" class=\"data row13 col4\" >1.08分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_2bef4_row14_col0\" class=\"data row14 col0\" >数据分析师</td>\n",
       "      <td id=\"T_2bef4_row14_col1\" class=\"data row14 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_2bef4_row14_col2\" class=\"data row14 col2\" >37500元</td>\n",
       "      <td id=\"T_2bef4_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row14_col4\" class=\"data row14 col4\" >1.05分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_2bef4_row15_col0\" class=\"data row15 col0\" >产品运营（偏数据分析）</td>\n",
       "      <td id=\"T_2bef4_row15_col1\" class=\"data row15 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_2bef4_row15_col2\" class=\"data row15 col2\" >27500元</td>\n",
       "      <td id=\"T_2bef4_row15_col3\" class=\"data row15 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row15_col4\" class=\"data row15 col4\" >1.02分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_2bef4_row16_col0\" class=\"data row16 col0\" >资深数据分析师（杭州）</td>\n",
       "      <td id=\"T_2bef4_row16_col1\" class=\"data row16 col1\" >2020-03-16 10:59:00</td>\n",
       "      <td id=\"T_2bef4_row16_col2\" class=\"data row16 col2\" >37500元</td>\n",
       "      <td id=\"T_2bef4_row16_col3\" class=\"data row16 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row16_col4\" class=\"data row16 col4\" >1.01分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_2bef4_row17_col0\" class=\"data row17 col0\" >大数据建模总监</td>\n",
       "      <td id=\"T_2bef4_row17_col1\" class=\"data row17 col1\" >2020-03-16 11:08:00</td>\n",
       "      <td id=\"T_2bef4_row17_col2\" class=\"data row17 col2\" >37500元</td>\n",
       "      <td id=\"T_2bef4_row17_col3\" class=\"data row17 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row17_col4\" class=\"data row17 col4\" >2.72分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_2bef4_row18_col0\" class=\"data row18 col0\" >数据建模专家-杭州-01546</td>\n",
       "      <td id=\"T_2bef4_row18_col1\" class=\"data row18 col1\" >2020-03-16 11:17:00</td>\n",
       "      <td id=\"T_2bef4_row18_col2\" class=\"data row18 col2\" >30000元</td>\n",
       "      <td id=\"T_2bef4_row18_col3\" class=\"data row18 col3\" >nan</td>\n",
       "      <td id=\"T_2bef4_row18_col4\" class=\"data row18 col4\" >3.03分</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2bef4_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_2bef4_row19_col0\" class=\"data row19 col0\" >数据分析专家（游戏业务）</td>\n",
       "      <td id=\"T_2bef4_row19_col1\" class=\"data row19 col1\" >2020-03-16 10:19:00</td>\n",
       "      <td id=\"T_2bef4_row19_col2\" class=\"data row19 col2\" >37500元</td>\n",
       "      <td id=\"T_2bef4_row19_col3\" class=\"data row19 col3\" >2号线</td>\n",
       "      <td id=\"T_2bef4_row19_col4\" class=\"data row19 col4\" >0.83分</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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     },
     "execution_count": 46,
     "metadata": {},
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   ],
   "source": [
    "# 定义格式字典\n",
    "format_dict = {\n",
    "    'salary': '{:.0f}元',      # 工资列：整数+“元”\n",
    "    'matchScore': '{:.2f}分'    # 匹配分列：两位小数+“分”\n",
    "}\n",
    "\n",
    "# 应用样式\n",
    "data.style.format(format_dict)"
   ]
  },
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   "metadata": {},
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    "![](http://liuzaoqi.oss-cn-beijing.aliyuncs.com/2021/09/16/16317972442543.jpg?域名/sample.jpg?x-oss-process=style/stylename)"
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   "metadata": {},
   "outputs": [],
   "source": []
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